{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"auto_encode_classification_images.ipynb","provenance":[{"file_id":"173_vpr3ItdJNyybO-Sbfh44EUaAjl_Iu","timestamp":1566774501982}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"uSbUFEsttU_F","colab_type":"code","colab":{}},"source":["import os\n","\n","import torch\n","import torchvision\n","from torch import nn\n","from torch.autograd import Variable\n","from torch.utils.data import DataLoader\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","from torchvision.utils import save_image\n","\n","if not os.path.exists('./mlp_img'):\n","    os.mkdir('./mlp_img')\n","\n","\n","def to_img(x, h, w):\n","    x = 0.5 * (x + 1)\n","    x = x.clamp(0, 1)\n","    x = x.view(x.size(0), 1, h, w)\n","    return x\n","\n","\n","num_epochs = 2\n","batch_size = 128\n","learning_rate = 1e-3\n","\n","img_transform = transforms.Compose([\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n","])\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DynMaOevuKA5","colab_type":"code","outputId":"c8f2feca-b7fe-42a9-cfc1-7182a9b461ea","executionInfo":{"status":"ok","timestamp":1569002183574,"user_tz":240,"elapsed":512,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5OzTqE9DuUBT","colab_type":"code","colab":{}},"source":["# # ls drive/My\\ Drive\n","# train_loader = torch.utils.data.DataLoader(\n","#     datasets.MNIST('../data', train=True, download=True,\n","#                    transform=transforms.ToTensor()),\n","#     batch_size=args.batch_size, shuffle=True, **kwargs)\n","# test_loader = torch.utils.data.DataLoader(\n","#     datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),\n","#     batch_size=args.batch_size, shuffle=True, **kwargs)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9WuxWeEGyb98","colab_type":"code","outputId":"1557842e-caca-4864-cc97-9d68807fe155","executionInfo":{"status":"ok","timestamp":1568223169895,"user_tz":240,"elapsed":2399,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# cp MNIST drive/My\\ Drive/MNIST -rf\n","ls drive/My\\ Drive/MNIST"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mMNIST\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BlwxYbAJtmTm","colab_type":"code","colab":{}},"source":["dataset = MNIST('./drive/My Drive/MNIST', train=True, transform=transforms.ToTensor(), download=True)\n","dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LVW8Wx4qwkuH","colab_type":"code","outputId":"7d3b0f90-f625-43cf-bbbd-6bb148113348","executionInfo":{"status":"ok","timestamp":1568221138778,"user_tz":240,"elapsed":2806,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["dd = next(iter(dataloader))\n","img, _  = dd\n","print(img.shape)\n","\n","\n","c = img.view([img.shape[0], -1])\n","c.shape\n","# a = torch.arange(1, 17)\n","# b = a.reshape([4,4])\n","# b.shape\n","# b.view()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["torch.Size([128, 1, 28, 28])\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 784])"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"-rIGyJGGMhYU","colab_type":"code","colab":{}},"source":["del dd"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"eZEhMHnD0Zwj","colab_type":"code","outputId":"73022b4f-dfc4-4acb-91d6-763ac4a08aa5","executionInfo":{"status":"ok","timestamp":1567695883326,"user_tz":240,"elapsed":166,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["dataloader.__dict__"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'_DataLoader__initialized': True,\n"," 'batch_sampler': <torch.utils.data.sampler.BatchSampler at 0x7f74cab65ac8>,\n"," 'batch_size': 128,\n"," 'collate_fn': <function torch.utils.data._utils.collate.default_collate>,\n"," 'dataset': Dataset MNIST\n","     Number of datapoints: 60000\n","     Root location: ./drive/My Drive/MNIST\n","     Split: Train,\n"," 'drop_last': False,\n"," 'num_workers': 0,\n"," 'pin_memory': False,\n"," 'sampler': <torch.utils.data.sampler.RandomSampler at 0x7f74cab65a90>,\n"," 'timeout': 0,\n"," 'worker_init_fn': None}"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"jlvyzuS_wscm","colab_type":"code","colab":{}},"source":["# for data in dataloader:\n","#   img, _ = data\n","#   print(img.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4aNppKkMtzEf","colab_type":"code","outputId":"0fbdba95-c508-43ed-b5b4-3b746a610f97","executionInfo":{"status":"ok","timestamp":1569000969109,"user_tz":240,"elapsed":25626,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["\n","class autoencoder(nn.Module):\n","    def __init__(self):\n","        super(autoencoder, self).__init__()\n","        self.encoder = nn.Sequential(\n","            nn.Linear(28 * 28, 128),\n","            nn.ReLU(True),\n","            nn.Linear(128, 64),\n","            nn.ReLU(True), \n","            nn.Linear(64, 12), \n","            nn.ReLU(True), \n","            nn.Linear(12, 9))\n","        self.decoder = nn.Sequential(\n","            nn.Linear(9, 12),\n","            nn.ReLU(True),\n","            nn.Linear(12, 64),\n","            nn.ReLU(True),\n","            nn.Linear(64, 128),\n","            nn.ReLU(True), \n","            nn.Linear(128, 28 * 28), \n","            nn.Tanh())\n","\n","    def forward(self, x):\n","        z = self.encoder(x)\n","        x = self.decoder(z)\n","        return x, z\n","\n","    def generate(self, code):\n","#         z = self.encoder(x)\n","        x = self.decoder(code)\n","        return x\n","      \n","\n","model = autoencoder().cuda()\n","criterion = nn.MSELoss()\n","optimizer = torch.optim.Adam(\n","    model.parameters(), lr=learning_rate, weight_decay=1e-5)\n","\n","for epoch in range(num_epochs):\n","    for data in dataloader:\n","#         import pdb; pdb.set_trace()      \n","        img,_ = data\n","\n","        img = img.view(img.size(0), -1)\n","        img = Variable(img).cuda()\n","        # ===================forward=====================\n","        output, _ = model(img)\n","        loss = criterion(output, img)\n","        # ===================backward====================\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","    # ===================log========================\n","    print('epoch [{}/{}], loss:{:.4f}'\n","          .format(epoch + 1, num_epochs, loss.data))\n","    if epoch % 10 == 0:\n","        pic = to_img(output.cpu().data, 28, 28)\n","        save_image(pic, './mlp_img/image_{}.png'.format(epoch))\n","\n","torch.save(model.state_dict(), './sim_autoencoder.pth')\n","# torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/sim_autoencoder.pth')\n"],"execution_count":4,"outputs":[{"output_type":"stream","text":["epoch [1/2], loss:0.0549\n","epoch [2/2], loss:0.0406\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BRW1b2njt0WS","colab_type":"code","colab":{}},"source":["# torch.__version__"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"x4CFQ1nwttnF","colab_type":"code","outputId":"c9f9fff3-a064-4a27-acfc-4658bb12e0cc","executionInfo":{"status":"ok","timestamp":1568316266361,"user_tz":240,"elapsed":43755,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# class autoencoder(nn.Module):\n","#     def __init__(self):\n","#         super(autoencoder, self).__init__()\n","#         self.encoder = nn.Sequential(\n","#             nn.Linear(28 * 28, 128),\n","#             nn.ReLU(True),\n","#             nn.Linear(128, 200))\n","#         self.decoder = nn.Sequential(\n","#             nn.Linear(200, 28 * 28), \n","#             nn.Tanh())\n","\n","#     def forward(self, x):\n","#         z = self.encoder(x)\n","#         x = self.decoder(z)\n","#         return x, z\n","\n","#     def generate(self, code):\n","# #         z = self.encoder(x)\n","#         x = self.decoder(code)\n","#         return x\n","\n","\n","\n","class autoencoder(nn.Module):\n","    def __init__(self):\n","        super(autoencoder, self).__init__()\n","        self.encoder = nn.Sequential(\n","            nn.Conv2d(1, 16, 3, stride=1, padding=1),  # b, 16, 10, 10\n","            nn.ReLU(True),\n","        )\n","        self.decoder = nn.Sequential(\n","            nn.ConvTranspose2d(16, 1, 3, stride=1, padding=1),  # b, 16, 5, 5\n","            nn.Tanh()\n","        )\n","\n","    def forward(self, x):\n","        z = self.encoder(x)\n","        x = self.decoder(z)\n","        return x, z\n","\n","    def generate(self, code):\n","#         z = self.encoder(x)\n","        x = self.decoder(code)\n","        return x\n","      \n","model = autoencoder().cuda()\n","criterion = nn.MSELoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,\n","                             weight_decay=1e-5)\n","\n","      \n","\n","# model = autoencoder().cuda()\n","# criterion = nn.MSELoss()\n","# optimizer = torch.optim.Adam(\n","#     model.parameters(), lr=learning_rate, weight_decay=1e-5)\n","\n","num_epochs= 10\n","for epoch in range(num_epochs):\n","    for data in dataloader:\n","#         import pdb; pdb.set_trace()      \n","        img,_ = data\n","\n","#         img = img.view(img.size(0), -1)\n","        img = Variable(img).cuda()\n","        # ===================forward=====================\n","        output, _ = model(img)\n","        loss = criterion(output, img)\n","        # ===================backward====================\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","    # ===================log========================\n","    print('epoch [{}/{}], loss:{:.4f}'\n","          .format(epoch + 1, num_epochs, loss.data))\n","    if epoch % 10 == 0:\n","        pic = to_img(output.cpu().data, 28, 28)\n","        save_image(pic, './mlp_img/image_{}.png'.format(epoch))\n","\n","torch.save(model.state_dict(), './sim_autoencoder.pth')\n","# torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/sim_autoencoder.pth')\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["epoch [1/10], loss:0.0151\n","epoch [2/10], loss:0.0054\n","epoch [3/10], loss:0.0036\n","epoch [4/10], loss:0.0027\n","epoch [5/10], loss:0.0022\n","epoch [6/10], loss:0.0019\n","epoch [7/10], loss:0.0016\n","epoch [8/10], loss:0.0014\n","epoch [9/10], loss:0.0012\n","epoch [10/10], loss:0.0011\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AnzbrFri4wQG","colab_type":"code","outputId":"7ea27ce0-a570-4035-d479-fa90b31a1fe3","executionInfo":{"status":"ok","timestamp":1568315744710,"user_tz":240,"elapsed":952,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":306}},"source":["from torchsummary import summary\n","summary(model, input_size=(1, 28, 28))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1           [-1, 16, 28, 28]             160\n","              ReLU-2           [-1, 16, 28, 28]               0\n","   ConvTranspose2d-3            [-1, 1, 28, 28]             145\n","              Tanh-4            [-1, 1, 28, 28]               0\n","================================================================\n","Total params: 305\n","Trainable params: 305\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.20\n","Params size (MB): 0.00\n","Estimated Total Size (MB): 0.21\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rrKKjecP047m","colab_type":"code","outputId":"b5e4a415-39bf-42d4-94cd-153fb3b0599e","executionInfo":{"status":"ok","timestamp":1568316396927,"user_tz":240,"elapsed":1463,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# loss.data\n","output, du = model(img)\n","du.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1000, 16, 28, 28])"]},"metadata":{"tags":[]},"execution_count":120}]},{"cell_type":"code","metadata":{"id":"4Qweo7mr3WgD","colab_type":"code","outputId":"7b3b2f16-3ddb-4a3b-8416-7d8af91e28d2","executionInfo":{"status":"ok","timestamp":1568316456318,"user_tz":240,"elapsed":1491,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# loss.data[0]\n","# model.decoder()\n","output.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1000, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":121}]},{"cell_type":"code","metadata":{"id":"Qhl12L3h3YF8","colab_type":"code","outputId":"5c13db7d-d8a9-4611-e17e-30be8a120548","executionInfo":{"status":"ok","timestamp":1568221928162,"user_tz":240,"elapsed":4412,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdata\u001b[0m/  \u001b[01;34mdrive\u001b[0m/  \u001b[01;34mmlp_img\u001b[0m/  \u001b[01;34msample_data\u001b[0m/  sim_autoencoder.pth\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LGwa_cef6LP8","colab_type":"code","outputId":"462494aa-843d-4d59-c663-06833606a7b6","executionInfo":{"status":"ok","timestamp":1568221931431,"user_tz":240,"elapsed":6323,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ls mlp_img/*"],"execution_count":0,"outputs":[{"output_type":"stream","text":["mlp_img/image_0.png\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3XDM-dEk6cHR","colab_type":"code","colab":{}},"source":["from matplotlib import pyplot as plt\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"53x80mMf6ldn","colab_type":"code","outputId":"7a4d744e-063c-4f73-8ac9-b733191d538c","executionInfo":{"status":"error","timestamp":1568221931460,"user_tz":240,"elapsed":4829,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":341}},"source":["\n","a = plt.imread('./mlp_img/image_9.png')\n","plt.imshow(a)"],"execution_count":0,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-43-70368b3b0394>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./mlp_img/image_9.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m   2150\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_dedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2151\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2152\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m   1364\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1366\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1367\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1368\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './mlp_img/image_9.png'"]}]},{"cell_type":"code","metadata":{"id":"3glOy35v60PU","colab_type":"code","colab":{}},"source":["# test the model\n","# output = model(img)\n","img.shape\n","c.shape\n","img.shape\n","output.shape\n","c.shape"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Xh8fj8PL7TrC","colab_type":"code","colab":{}},"source":["\n","for i in range(10):\n","  plt.figure()\n","  c = img[i].cpu().detach()\n","  c = c.reshape([28,28])\n","  plt.imshow(c.numpy())\n","  \n","  \n","  plt.figure()\n","  output, code = model(img[i])\n","  a = output.reshape([28,28])\n","  a = a.cpu().detach()\n","  plt.imshow(a.numpy())\n","  \n","  \n","  plt.figure()\n","  c = code.cpu().detach()\n","  c = c.reshape([3,3])\n","  plt.imshow(c.numpy())\n","  "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1hOFtDbnRwhL","colab_type":"code","colab":{}},"source":["output, code = model(img[i])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"f1f4rklXRxyI","colab_type":"code","colab":{}},"source":["code"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LDFm44JfIJH6","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"S1LkgrGDRxue","colab_type":"code","outputId":"513bd738-af3d-4c16-95db-c704d7c1ea80","executionInfo":{"status":"error","timestamp":1569002382598,"user_tz":240,"elapsed":1045,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":266}},"source":["latent_d = 3\n","import torch.nn.functional as F\n","\n","\n","class VAE(nn.Module):\n","    def __init__(self):\n","        super(VAE, self).__init__()\n","\n","        self.fc1 = nn.Linear(784, 400)\n","        self.fc21 = nn.Linear(400, latent_d)\n","        self.fc22 = nn.Linear(400, latent_d)\n","        self.fc3 = nn.Linear(latent_d, 400)\n","        self.fc4 = nn.Linear(400, 784)\n","\n","    def encode(self, x):\n","        h1 = F.relu(self.fc1(x))\n","        return self.fc21(h1), self.fc22(h1)\n","\n","    def reparametrize(self, mu, logvar):\n","        std = logvar.mul(0.5).exp_()\n","        if torch.cuda.is_available():\n","            eps = torch.cuda.FloatTensor(std.size()).normal_()\n","        else:\n","            eps = torch.FloatTensor(std.size()).normal_()\n","        eps = Variable(eps)\n","        return eps.mul(std).add_(mu)\n","\n","    def decode(self, z):\n","        h3 = F.relu(self.fc3(z))\n","        return F.sigmoid(self.fc4(h3))\n","\n","    def forward(self, x):\n","        mu, logvar = self.encode(x)\n","        z = self.reparametrize(mu, logvar)\n","        return self.decode(z), mu, logvar\n","\n","\n","model = VAE()\n","if torch.cuda.is_available():\n","    model.cuda()\n","\n","reconstruction_function = nn.MSELoss(size_average=False)\n","\n","\n","def loss_function(recon_x, x, mu, logvar):\n","    \"\"\"\n","    recon_x: generating images\n","    x: origin images\n","    mu: latent mean\n","    logvar: latent log variance\n","    \"\"\"\n","    BCE = reconstruction_function(recon_x, x)  # mse loss\n","    # loss = 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n","    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)\n","    KLD = torch.sum(KLD_element).mul_(-0.5)\n","    # KL divergence\n","    return BCE + KLD\n","\n","\n","optimizer = torch.optim.Adam(\n","  model.parameters(), lr=1e-3, weight_decay=1e-5)\n","# optimizer = optim.Adam(model.parameters(), lr=1e-3)\n","\n","num_epochs = 1\n","for epoch in range(num_epochs):\n","    model.train()\n","    train_loss = 0\n","    for batch_idx, data in enumerate(dataloader):\n","        img, _ = data\n","        img = img.view(img.size(0), -1)\n","        img = Variable(img)\n","        if torch.cuda.is_available():\n","            img = img.cuda()\n","        optimizer.zero_grad()\n","        recon_batch, mu, logvar = model(img)\n","        loss = loss_function(recon_batch, img, mu, logvar)\n","        loss.backward()\n","        train_loss += loss.data#[0]\n","        optimizer.step()\n","        if batch_idx % 100 == 0:\n","            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch,\n","                batch_idx * len(img),\n","                len(dataloader.dataset), 100. * batch_idx / len(dataloader),\n","                loss.data / len(img)))\n","                \n","#                 loss.data[0] / len(img)))\n","\n","    print('====> Epoch: {} Average loss: {:.4f}'.format(\n","        epoch, train_loss / len(dataloader.dataset)))\n","    if epoch % 10 == 0:\n","        save = to_img(recon_batch.cpu().data, 28, 28)\n","#         save_image(save, './vae_img/image_{}.png'.format(epoch))\n","\n","# torch.save(model.state_dict(), './vae.pth')\n","# torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/vae.pth')\n"],"execution_count":8,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n","  warnings.warn(warning.format(ret))\n"],"name":"stderr"},{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-8-3b3c9eefc314>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: 'module' object is not iterable"]}]},{"cell_type":"code","metadata":{"id":"b0Yu4Af5SgDG","colab_type":"code","colab":{}},"source":["mu.shape"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rqTJojLb7WWT","colab_type":"code","outputId":"904e647c-baa3-4c71-b68d-efbbb52b08ea","executionInfo":{"status":"ok","timestamp":1569002391650,"user_tz":240,"elapsed":884,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)\n","    \n","    \n","class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n","        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 500)\n","        self.fc2 = nn.Linear(500, 10)\n","\n","    def forward(self, x):\n","        x_1 = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x_1, 2, 2)\n","        x_2 = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x_2, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x_3 = F.relu(self.fc1(x))\n","        h = F.softmax(self.fc2(x_3), dim=1)\n","        return h, x_3, x_2, x_1      \n","        \n","#         x = self.fc2(x_3)\n","#         return F.softmax(x, dim=1)\n","      \n","#     def forward(self, x):\n","#         x_1 = F.relu(self.conv1(x))\n","#         x = F.max_pool2d(x_1, 2, 2)\n","#         x_2 = F.relu(self.conv2(x))\n","#         x = F.max_pool2d(x_2, 2, 2)\n","#         x = x.view(-1, 4*4*50)\n","#         x_3 = F.relu(self.fc1(x))\n","# #         x = self.fc2(x)\n","# #         return F.softmax(x, dim=1)\n","#         h = F.sigmoid(self.fc2(x_3))\n","#         return h, x_3, x_2, x_1      \n","      \n","    \n","model_n = Model()\n","if cuda:\n","    model_n.cuda() # CUDA!\n","optimizer = optim.Adam(model_n.parameters(), lr=1e-3)      \n"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5H5yegKTF282","colab_type":"code","outputId":"a765c6df-5dfb-462b-ebb6-139fbf87e9ec","executionInfo":{"status":"ok","timestamp":1569002397242,"user_tz":240,"elapsed":3453,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["9920512it [00:01, 9004825.81it/s]                            \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 0/28881 [00:00<?, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["32768it [00:00, 136087.33it/s]           \n","  0%|          | 0/1648877 [00:00<?, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["1654784it [00:00, 2586583.39it/s]                           \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["8192it [00:00, 51554.89it/s]            \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Processing...\n","Done!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"HH3K6tswLKZR","colab_type":"code","outputId":"36f56797-5662-42e2-9bbc-35b3a05c3380","executionInfo":{"status":"error","timestamp":1568312875744,"user_tz":240,"elapsed":1422,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["# del dataset\n","# del train, test\n","# del data, target\n","del  evaluate_x,  evaluate_y"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-10-b515f1f8a2c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m  \u001b[0mevaluate_x\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mevaluate_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'evaluate_x' is not defined"]}]},{"cell_type":"code","metadata":{"id":"iWT7A9Hk7YO4","colab_type":"code","outputId":"f1abcb08-7d67-4069-df92-98102bd78a06","executionInfo":{"status":"ok","timestamp":1569002518763,"user_tz":240,"elapsed":115700,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["EPOCHS = 15\n","losses = []\n","\n","model_n.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred, _, _, _ = model_n(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model_n.eval()\n","    output, _, _, _ = model_n(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":11,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/15 [60000/60000 (100%)]\tLoss: 1.508591\t Test Accuracy: 94.3600%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/15 [60000/60000 (100%)]\tLoss: 1.490447\t Test Accuracy: 97.6900%\n"," Train Epoch: 3/15 [60000/60000 (100%)]\tLoss: 1.461472\t Test Accuracy: 98.1200%\n"," Train Epoch: 4/15 [60000/60000 (100%)]\tLoss: 1.484659\t Test Accuracy: 98.3900%\n"," Train Epoch: 5/15 [60000/60000 (100%)]\tLoss: 1.488232\t Test Accuracy: 98.0700%\n"," Train Epoch: 6/15 [60000/60000 (100%)]\tLoss: 1.480929\t Test Accuracy: 98.5000%\n"," Train Epoch: 7/15 [60000/60000 (100%)]\tLoss: 1.486126\t Test Accuracy: 98.8900%\n"," Train Epoch: 8/15 [60000/60000 (100%)]\tLoss: 1.461634\t Test Accuracy: 98.9400%\n"," Train Epoch: 9/15 [60000/60000 (100%)]\tLoss: 1.461315\t Test Accuracy: 98.7800%\n"," Train Epoch: 10/15 [60000/60000 (100%)]\tLoss: 1.461928\t Test Accuracy: 98.6600%\n"," Train Epoch: 11/15 [60000/60000 (100%)]\tLoss: 1.461283\t Test Accuracy: 98.9700%\n"," Train Epoch: 12/15 [60000/60000 (100%)]\tLoss: 1.461338\t Test Accuracy: 98.7200%\n"," Train Epoch: 13/15 [60000/60000 (100%)]\tLoss: 1.474485\t Test Accuracy: 99.2100%\n"," Train Epoch: 14/15 [60000/60000 (100%)]\tLoss: 1.471213\t Test Accuracy: 99.0500%\n"," Train Epoch: 15/15 [60000/60000 (100%)]\tLoss: 1.461397\t Test Accuracy: 98.9600%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BifoP9dtHLJ3","colab_type":"code","outputId":"2e16944b-d9b3-4225-8326-3bd035ee71eb","executionInfo":{"status":"error","timestamp":1568316081417,"user_tz":240,"elapsed":1494,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["\n","latent_d = xx\n","z_code = torch.randn(100, latent_d).cuda()   #for vae\n","# evaluate_x[:,None,...].shape\n","dd = model.decode(z_code)\n","# mu.shape"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-112-388c5e8fdfae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlatent_d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mz_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#for vae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# evaluate_x[:,None,...].shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'xx' is not defined"]}]},{"cell_type":"code","metadata":{"id":"epSZs0a-HNAv","colab_type":"code","outputId":"2ebc9ed6-7231-44d7-b71a-d13b6758acdf","executionInfo":{"status":"error","timestamp":1568316018028,"user_tz":240,"elapsed":1455,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["latent_d = 90\n","z_code = torch.randn(100, latent_d).cuda()   #for vae\n","dd  = model.generate(z_code)  # autoencoder\n","dd.shape\n","\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-110-ffe7cd538e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlatent_d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mz_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#for vae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdd\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_code\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# autoencoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 539\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'autoencoder' object has no attribute 'generate'"]}]},{"cell_type":"code","metadata":{"id":"xaSluXlALTL0","colab_type":"code","outputId":"f694104d-f869-4d7f-eae9-bc6ae0f42f99","executionInfo":{"status":"error","timestamp":1568316312032,"user_tz":240,"elapsed":1455,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["latent_d = 16 # conv autoencode\n","z_code = torch.rand(100, latent_d).cuda()   #for vae\n","dd  = model.decode(z_code)  # autoencoder\n","dd.shape\n","\n","\n","# dd.shape\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-119-1bd66157a3bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlatent_d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;31m# conv autoencode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mz_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#for vae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdd\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_code\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# autoencoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 539\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'autoencoder' object has no attribute 'decode'"]}]},{"cell_type":"code","metadata":{"id":"5Ekmv3A5Shh4","colab_type":"code","colab":{}},"source":["for i in range(10):\n","  plt.figure()\n","  idx = np.where(target.cpu()==i)\n","  # torch.mean(dd[idx,:], dim=1).shape\n","  # dd[idx,:].mean(dim=0).shape\n","  plt.imshow(dd[idx,:].mean(dim=1).detach().cpu().reshape(28,28))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hBpqTBW9IA-E","colab_type":"code","outputId":"422ebf3e-a37e-4429-9d84-fa6fd28aa229","executionInfo":{"status":"ok","timestamp":1568315062900,"user_tz":240,"elapsed":2893,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":317}},"source":["# avgs_z = {}\n","# for i in range(10):\n","#   avgs_z[i] = []\n","\n","import matplotlib.pyplot as plt\n","f, axarr = plt.subplots(4, 10)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(5)\n","f.set_figwidth(15)\n","f.subplots_adjust(hspace=0.1, wspace=0.3, right = 0.8)\n","\n","\n","for kk in range(40):\n","#   plt.figure()\n","  axarr[kk//10,kk%10].imshow(dd[kk].detach().cpu().view(28,28))\n","  axarr[kk//10,kk%10].axis('off')\n","#   avgs_z[target[i]].append()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAvwAAAEsCAYAAAC/jmc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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LiHQuw99ph8EPC60/fDmMXNaPuWdPG5tfiI7w66STTjrppJNOOumk0xlMnyvCH9hF1LyI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VjunIes9TkwFR0seRnj1XIZdCvNj/e9L6Ct6OTUBbEZn/QE/eQjZSySth/64e5n/NwsZiua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\nAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x360 with 40 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"s5YzMFbIG-8T","colab_type":"code","colab":{}},"source":["#   plt.figure()\n","#   output, code = model(img[i])\n","#   a = output.reshape([28,28])\n","#   a = a.cpu().detach()\n","#   plt.imshow(a.numpy())\n","  \n","  \n","#   plt.figure()\n","#   c = code.cpu().detach()\n","#   c = c.reshape([3,3])\n","#   plt.imshow(c.numpy())\n","  \n","  \n","  \n","  # vae\n","  \n","  i = 2\n","  plt.figure()\n","  plt.imshow(img[i].cpu().reshape(28,28))\n","  \n","#   recon_batch, mu, logvar\n","  plt.figure()\n","  output, code, var = model(img[i])\n","  a = output.reshape([28,28])\n","  a = a.cpu().detach()\n","  plt.imshow(a.numpy())\n","  \n","  \n","  plt.figure()\n","  c = code.cpu().detach()\n","  c = c.reshape([4,5])\n","  plt.imshow(c.numpy())\n","  \n","  plt.figure()\n","  tt = model.decode(c.reshape(1,-1).cuda())\n","  plt.imshow(tt.cpu().detach().numpy().reshape(28,28))\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uuVbdpXWQuQy","colab_type":"code","colab":{}},"source":["target"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"PE4-LAfIMMuz","colab_type":"code","colab":{}},"source":["# c.shape\n","# img[i].shape\n","dd.shape\n","c.shape\n","\n","# tt = model.decode(c.view(1,-1).cuda())\n","# tt.shape\n","# c.view(1,-1).shape\n","\n","c.min()\n","torch.rand(1, 20).max()\n","# help(torch.randn)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KBfxChzBUMCM","colab_type":"code","colab":{}},"source":["ttrecon_batch, mu, _ = model(img)  "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YC7gWwOpMw3e","colab_type":"code","colab":{}},"source":["# del all_preds\n","# locals()\n","# mu[2].max()\n","z = torch.randn(1, 20).cuda()\n","sample = model.decode(z).cuda()\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JCj_sMDbWA7p","colab_type":"code","colab":{}},"source":["sample.shape\n","plt.imshow(sample.detach().cpu().view(28,28))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3DVNiiJMGkwt","colab_type":"code","colab":{}},"source":["# # clean one\n","\n","# batch_size = 1000\n","# all_size = 10000\n","# iters = 10\n","\n","# stats = dict()\n","# for i in range(10):\n","#     stats[i] = 0    \n","    \n","# avgs = torch.zeros(iters, 10, 28*28)\n","# avgs_codes = torch.zeros(iters, 10, 20)\n","\n","# for kk in range(iters):\n","#   print(kk)\n","  \n","#   with torch.no_grad():\n","#     # vae\n","#     z_code = torch.randn(all_size, 20).cuda()\n","#     z = model.decode(z_code).cuda()\n","    \n","#     # auto\n","# #     z_code = torch.rand(all_size, 9).cuda()\n","# #     z = model.generate(z_code)\n","\n","#     z = z[:,None,...].view(-1,1, 28,28)\n","#   #   z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","#     z.cuda()\n","\n","# #     del z_code\n","  \n","  \n","#   all_preds = []\n","# #   all_confs = []\n","  \n","#   for k in range(0,all_size, batch_size):\n","#       y_pred, _, _, _ = model_n(z[k:k+batch_size].cuda())\n","#       recon_batch, mu, _ = model(img)        \n","#       conf, pred = y_pred.data.max(1)\n","      \n","      \n","#       all_preds.append(pred)\n","# #       all_confs.append(conf)\n","  \n","#   preds = torch.cat(all_preds)\n","# #   confs = torch.cat(all_confs)    \n","  \n","# #   weighting\n","# #   tt = confs[:,None].repeat(1,784)\n","# #   uu = z.view(-1,28*28)\n","# #   z = uu* tt.type(torch.FloatTensor)\n","# #   z = z.view(-1,1,28,28)\n","\n","#   for i in range(10):\n","#     stats[i] += torch.sum(preds==i)\n","#     a = torch.mean(z[preds==i] , dim=0) \n","#     avgs[kk, i] = a.reshape(28*28)\n","#     b = torch.mean(z_code[preds==i] , dim=0) \n","#     avgs_codes[kk, i] = b\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wm0tIpTWPOQf","colab_type":"code","outputId":"07900dcd-ea11-4717-cca0-108fbfb08e25","executionInfo":{"status":"ok","timestamp":1568315128564,"user_tz":240,"elapsed":6900,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# clean one\n","\n","batch_size = 1000\n","all_size = 1000000\n","iters = 1\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","avgs_codes = torch.zeros(iters, 10, latent_d)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  with torch.no_grad():\n","#     # vae\n","#     z_code = torch.randn(all_size, latent_d).cuda()\n","#     z = model.decode(z_code).cuda()\n","    \n","    # auto\n","    z_code = torch.randn(all_size, 200).cuda()\n","    z = model.generate(z_code)\n","\n","    z = z[:,None,...].view(-1,1, 28,28)\n","  #   z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","    z.cuda()\n","\n","#     del z_code\n","  \n","  \n","  all_preds = []\n","#   all_confs = []\n","  \n","  for k in range(0, all_size, batch_size):\n","      y_pred, _, _, _ = model_n(z[k:k+batch_size].cuda()) # for prediction\n","#       recon_batch, mu, _ = model(img)        \n","      conf, pred = y_pred.data.max(1)\n","      \n","      \n","      all_preds.append(pred)\n","#       all_confs.append(conf)\n","  \n","  preds = torch.cat(all_preds)\n","#   confs = torch.cat(all_confs)    \n","  \n","#   weighting\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z.view(-1,28*28)\n","#   z = uu* tt.type(torch.FloatTensor)\n","#   z = z.view(-1,1,28,28)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(preds==i)\n","    a = torch.mean(z[preds==i] , dim=0) \n","    avgs[kk, i] = a.reshape(28*28)\n","    b = torch.mean(z_code[preds==i] , dim=0) \n","    avgs_codes[kk, i] = b\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ifDl-l01QhR-","colab_type":"code","colab":{}},"source":["# # y_pred[0]\n","# z_code = torch.rand(1, 9).cuda()\n","# z = model.generate(z_code)\n","# plt.imshow(z.detach().cpu()[0])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ob8X4femReGm","colab_type":"code","outputId":"0296836f-6f0d-4d64-f917-0c6c6298047a","executionInfo":{"status":"ok","timestamp":1568314183075,"user_tz":240,"elapsed":1508,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# z_code\n","stats"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(12404, device='cuda:0'),\n"," 1: tensor(0, device='cuda:0'),\n"," 2: tensor(0, device='cuda:0'),\n"," 3: tensor(0, device='cuda:0'),\n"," 4: tensor(17552, device='cuda:0'),\n"," 5: tensor(0, device='cuda:0'),\n"," 6: tensor(0, device='cuda:0'),\n"," 7: tensor(44088, device='cuda:0'),\n"," 8: tensor(25432, device='cuda:0'),\n"," 9: tensor(524, device='cuda:0')}"]},"metadata":{"tags":[]},"execution_count":60}]},{"cell_type":"code","metadata":{"id":"cC92eZhMQzhI","colab_type":"code","colab":{}},"source":["# z[0].shape\n","z[0].detach().cpu().shape"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ENFoHSz3Gktb","colab_type":"code","outputId":"def9a108-d56b-417c-c518-ed0b5adfc8f8","executionInfo":{"status":"error","timestamp":1568221931924,"user_tz":240,"elapsed":997,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["# code.shape\n","z_code.shape\n","# z[:,None,...].view(-1,1, 28,28).shape\n","len(y_pred)\n","y_pred[0].shape\n","z.shape\n","torch.rand(all_size, 1, 28, 28).dtype\n","z.dtype\n","y_pred.data"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-44-ae4ffcca404a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"]}]},{"cell_type":"code","metadata":{"id":"tOYV5mClGkrz","colab_type":"code","outputId":"0c318132-8279-4123-9a6f-237fe16fb22e","executionInfo":{"status":"ok","timestamp":1568315143436,"user_tz":240,"elapsed":1927,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":880}},"source":["# plotting  \n","save_path = 'drive/My Drive/classification_images/10way-weighted-2Run'\n","import os\n","\n","\n","# computing the average mnist images\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(5)\n","f.set_figwidth(15)\n","f.subplots_adjust(hspace=0.36) #, wspace=0.0, right = 0.8)\n","\n","\n","# mean_imgs = []\n","# for i in range(10):\n","#   plt.figure()\n","#   m = torch.mean(train.data[train.targets == i].type(torch.FloatTensor), dim=0)\n","#   mean_imgs.append(m)\n","#   axarr[i//5,i%5].imshow(m) \n","  \n","\n","# avgs[avgs != avgs] = 0 \n","\n","dd = torch.mean(avgs, dim=0)\n","# dd = nanmean(avgs.detach(), 0)\n"," \n","for kk in range(10):  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  \n","  a = (a -a.min()) / (a.max() -a.min())\n","  a = a.view(-1,28)\n","  \n","  print(a.min(), a.max())\n","  b, _, _, _ = model_n(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  conf, c = b.data.max(1) #[1]\n","#   axarr[kk//5,kk%5].set_title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {str(conf.cpu().data[0].numpy())} -  |pred|: {stats[kk]}')  \n","  axarr[kk//5,kk%5].set_title(f'gt: {str(kk)} - pred: {str(c.cpu().data[0].numpy())} - |pred|: {stats[kk]}')    \n","  axarr[kk//5,kk%5].imshow(a.detach()) #, cmap = 'gray')\n","  axarr[kk//5,kk%5].axis('off')\n","#   fig.savefig(os.path.join(save_path, str(kk)+'-.png'))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(0.) tensor(1.)\n","tensor(nan) tensor(nan)\n","tensor(nan) tensor(nan)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(nan) tensor(nan)\n","tensor(nan) tensor(nan)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3sAAAE/CAYAAAD/m9qwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns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jM+esYfIb9Lt6Fzg25DokPnzz+i7Zf7m4xTBIAxY+p/xkUHlNwxWBjJQwgh\nhMwFG7fN+julzCyn8Gg0rlbPGv2bjT3bfLmeK1Q+O3n+/E6dPjIW9UUaKdHP5eObdUd6NuiOnLZO\n70Nw8IGVSrbP5c91XBWp0yV1P+ItJpZaoW5T/TnNSm7uKFNyTky3MbcpuodA3PhU9a2y85FTL85e\nskT3OdeEIU7pYULFzmgZQxX6oNKDen7bt1ynL9vWpuQD11crOdaj29SxpzJSZ2q99uPLMUP3uce0\nzpVfqOfthaajL135nJLvkksjdRY2aafc4gqtg8MVuhHdJlZfUZOWZcyeNz3fBYDcpP6tbbO5vheh\nzvLLHiGEEEIIIYRkIVzsEUIIIYQQQkgWwsUeIYQQQgghhGQhi8Znr7RAx+NImrh6NQlt13uwxBhB\nA1ha0avk9j7tR3ZxXaOS7zl7sy6gUfsJrjlb27NfUvx8pM47jl+o5Ly4tnEv2aQN2u/ZebqSH7rm\nc0r+aOtWJSdrjIMCgLF8E7MlZWKE9Jq4eSbeStOVOt7K2GodW2VsMKo2m9Zpw+gNJa1KbovrmEKE\nEELIXHHmcZRn4rg580p7WIeWAwAk1+lMuXnaZ6eiRKe3xXUheQOmEuPSY30AASB30MT/M+2qWKUd\n//Ye0XsExE38wCWP6Of6saGo/9zASu3XlWNi3InJUhHXPldvOPv3Sv7+kfOV/Fzvikid/TmTx5Ab\nK1h8McvmSsq4SuYNmHS9XQRGC6I+YoNLJvcbS8V0+kiN9hstf17reOslZhzKokrbvUv7hdprLVdv\n/4AP/+rVSn7D1vuU/N2HL1Jy9WBUF4aW6Hn6wFLTbyPmd1ofPq1wMqrTB+21C2C4QrdjpNT69S2+\n72SLr8WEEEIIIYQQQqaEiz1CCCGEEEIIyUK42COEEEIIIYSQLISLPUIIIYQQQgjJQhbNBi01Bf1K\nfuaR1Up+aIl2Pt20qiVSRv+o9h4tKdCbuPzvk2cpOTakHTnHEtppc+/zy5R8a340IGTSRHQ9Y/lR\nJZ9dpjd5aazSDrCFop2b729Zo2RJRp10473aIbVovw44336O9gTvW6HLKN+tnVGPVeoNWyLO6ACw\nTos/fFQ7bp+2VgeEJYQQQuaMeQTGeswmD04f0F8f3dQMKX3M2Sv0c/lov96cLVGn5yODXXpnk0ST\nnmsUtEY3nshJ6t9sMOjV5TqA9TOD+jkc0/vNIf9Xjyq5Ju+CSJ2Hq/V8Ikfv14LCDV1K/qNqHc37\nv4+freSjvSVKzusx0cARGR4UtuhfkmU45UjpoUTxUbOJiNlVqL8uWkbBcX0e7cY3tQ9rBck9oOee\nsmytkuNteuySY9MIHG6urdFKs6mLua5+ckDrT+kuvVNNKi96nfTV6WPspjBdZxklNpQ/refgwyZW\nfH5XtM5kmW539aN6PPpWMqg6IYQQQgghhJAFABd7hBBCCCGEEJKFcLFHCCGEEEIIIVnIovHZO9yj\nDbtzjT9dckB35aln66css2iptru/4UIdMDRmjKBve+jFSr5g434lX1v1bKSO/9h5tZKdsXF+ee3T\nSj6e1MHHP3D0MiVvrNbByp+6T/v4AcBAlbVB1v4G+X3aJ28sodf8sQGdP79WR/wc7ooGa823xv/G\n9nrvUybY6pWRIgghhJAZIebR43KML5OJhxzrjr7jzlum/feXJrS/0+uWPKzk/Ukd4PzeNu1w11an\n9xDoeaA2Umflbj2/GOrQPlMpM1cY7tOOXrWNuuN5y7Vj1+igDQQNSErXMWzmCqMdut33tJ+h5Cca\nVyo5N6b7kJPGHdJ0AyO6ChQeOfWCqlt/SzMsiPVpuXxPtAyXq89bskSf6K7T9Vwyd7X20ZMxnX/s\nDF3puhrtMwoA+x/V4x/TU2gMm7HO7dfX2sgjer+IoTVaYQpaoj6fI2W6nbkmAL31yetdrY8vatV1\n9DXoNiV7p/a/c6ZZlbvSKPoCh1/2CCGEEEIIISQL4WKPEEIIIYQQQrIQLvYIIYQQQgghJAtZND57\nPb2FShazTJWEtqG9ckPUyPmRo9reuL9P+55tb9XB4ppatX3x1rN3KXlZvFvJX2vUPn0A8Np1Tyj5\n2zt17Jt/ffRaJbsObZd/2qbDSt77hO4DlkTt8pPl2gZ54Dpt5DxkbP9Xf1PnH67UapE8qs994ZGo\nXXXqdF1njrHlT52CsXQIIYTMLxF38UHjh1ZkYoHVD0XKKEskdRnG+ez4qI4n91i33hPgtUsfUfKh\nZLWS7xi5KlLnQI2exJQc0s/yJ59Ya9L18aMJfbwr0z5abZtMMDcAqQLdL5dvfI9MbLUnt2tfRPvk\nT8X1uS5tilSJnnX6mJwOG2dv8cUsmys5Wt0iOjtotmLo2hid58Xb9WjEjYtdcYu+MBL/rf1Oe153\nsZJHBnRsyMOd5ZE6azfrPSM6Hlyq5IKDOiaeDbI4uErH4cst0XL5eh0TGgCO7NH+sWNxcz0Xmph4\nT+pzOVymr5v8Tn185S4zGACartT9GDOX0lDZ4vtOtvhaTAghhBBCCCFkSrjYI4QQQgghhJAshIs9\nQgghhBBCCMlCFo3P3li7Npp90dbnlPxQY4OSd3dF49r0HtV29zlDeq17uE/bBucM6vQxEzDmew9e\nouSyZ6O+bNtepe2mr1yrfQm3HViv5PwmXefzTvvorb1zUMmNL9f+dAAwWmDi1ozpMt2o7kdPvbFP\nNmH0XLG2qx6ujL4jeOox7V+QKtd5kOR7BUIIIScWG5OsWz9SkTNqnodtUV+24336GfiLbh0Mbnux\niVEmusz/wdlKfvaY9mWKd0RjySW6tB9W+0Y9f8gZMfFwTbMHluhnatkunX9Eh9f1ZQ7qZ78bMX5f\nbbrMwlYT40yHI0T/Mn28pKL9LN1r4gUaVzAbq+1UoKBdj+3RS/Q45Hfrc5Y4Fp1bWh+9WL8+94lj\neq5oSXRqf83yR7XP3uASLQNA7BJ9sa24wuwp8fwyfXyX0a8K7S+bPKbnr7HqqG9irpmn5w1Ynz3j\no2t8+HLMVNTOb/uWGz9DAFU7TJkJXWa8d/HFhuQMnBBCCCGEEEKyEC72CCGEEEIIISQL4WKPEEII\nIYQQQrKQReOzhzJtePu7h09XsotpG9qO/Kgvm6WwXsf06G/SPn3rN2t75H3dOnZOyXITE2SnjssH\nAI17lyi5tWW5kkdqtN10zJhmVz2hbYVbL9C+BNYeGQAKjhufvGJt7F+0T9soJ41tf1+9tpvO6dJq\nkopH7arLG7p0Gf3aMLpqqU4nhBBC5krKuNyU7dWymMdVz+roO27p1L/ZSHx9BwtMBi0+MaoDo9k2\nFUZDeWGoQtdZcMzEBzOx1gbq9cO+2DzH27doZzgx8QcBINf47KWMH2DJQd2G/H598mxswITxRRwp\njMbMGzOnzs5Zhkw/TwWSxfo81T6mz7P1fezYEJ2q29h8/XW6zLL9Js+lW5QYb9Xxl3Pq9USwoDU6\nlof26Plsvon1h1qtdKNF5uIzPnqFddoH8Hivnt8CQNFh3Y4RHU4SJUe1nN+jz0tvvdbZomadHu+O\nzme71+p+5Rm/0s5liy82JL/sEUIIIYQQQkgWwsUeIYQQQgghhGQhXOwRQgghhBBCSBayaHz2aqu1\nf1xbq46Jl8rXdrfDQ9HYGZXLtd/Y6Ji2y33P1f+j5AsK9iv5wrgu823NFyv5iZdHDfOL7tU2zv0N\n2qZ52W/1erunQefv2KTti1MJ3c+KHdH1et9V2sBY2rTRfMRm/kXabnpDbZuSD2zTjUquj8Zv2Vzb\nouTte9cp2bnFZ+NMCCFkYTNmwoElyyaPs2Xj8gFRH7uiA3pqZGNz2TJHi/RzOtan29B5ejQuV1Gz\nPmZIT2kQ7zDx6Wp1GYM1Wi48YvJ3RescWKn3CLA+V7lJ4++0XKcny3R5ZfvMvKsi+pwfMrHTippN\nbD7dpFMCe55iJnYczHzJ+kYCwHClPsb6fDZvLZg0XUZ1uh2HYb1FBQCg6jE9dn2rdHrpbn3d9F+o\n/QJPW3YsWmiI3U+uivw2tFbrT/FBEwuyTad3nmZ1Vve7/PnJ/VDT5Ukc1+ljicU3n+WXPUIIIYQQ\nQgjJQrjYI4QQQgghhJAshIs9QgghhBBCCMlCFo3PXl6OtrM9/0W7lfzQ7jVKlpyojXNno46DV7de\nG+LesvsyJf9fuVTJ8Zj2txtMakP/wUHjPAAgtVFH7LGWvrmmjDxt4oyxEhOHr1T7BQ6+JGrwnvOM\njpeSW2hi4VzSq+s4qGObDFbqNg3VaQeFpVUmviCAPV3GhzKp7abtuSKEEELmio3jNlJq/G3arD9U\ntAzrzzRSpPPkd+t0lzO5j5Uz4cdS+dFKrb+TjQdY/ZTx0TtuCjVNGC3Qxw8si1SJxFE95cs3j/LO\nM4yvmPFVsnUOl+sfkiXRfkpKH2Pj/+UOR9uZ7TjzmaVzgz5HpQd0esoMPQCUHdBzP+tfWbFbK1Si\nLU1Q5hDJcq0bOaPRb0EDS8x4l+k64h06jzumnV0PxvUcPDdX508VReezdffofuWM6mN66nV66QHj\nw3e6iaFp4lta30cAyDWBNl2e9XeMZFnw8MseIYQQQgghhGQhXOwRQgghhBBCSBbCxR4hhBBCCCGE\nZCFc7BFCCCGEEEJIFrJoNmjpH9abn7Tmlig5Xqy9fJPHCiNlnLtln5IfP6A9pAuKdBkp441cbeqo\nLtTByzc16MDiAPB8r9645HCPdlBt3lqp5Hinzl9X367kI/t0eSMx49UNIC+hnUnHavWmLjm7ipVc\ncY6u43ivTq9frb20Dzaa6K8AJK6dZjc06AivzzfXRvIQQgghcyHHbPCRMrOasbiWcwejZQzWTB4k\n2eXq9BE9/cBwpX4O51TrRuXkRJ/TI926YQVNuuFJU0dJs94VYqBab0xhN3jJs4G6EdlHBgN1eq5Q\n3Gg24Ci3ZWp5qEofX9gaqRKjJiC9Dcwe09OoU4I8swFIKt/ol94zD4XHo/rTfoYe/6Ijeiztpi7d\nq/Uc2m4iNFpkN1+JVIlUTNdRcEx/L0qZPQqLD+j0nlK9m1K8SW/cV5BMo7Pm2hms1GVW7NIbzxzf\nosuMd+ny+ut0HcVN0U2F7AY6PWu1XLI/zS5PCxx+2SOEEEIIIYSQLISLPUIIIYQQQgjJQrjYI4QQ\nQgghhJAsZNH47HUf1gbE3U7LztgSb9jYFCnjuWNLlFxV2afkvkFtQz/SqH3X9hcZQ2pjXpy3MWpX\nXZynbffryzqUnNKx4NH/eLWSewa1wftZZx1U8rOPN0TqdKu0U8K5K/W5OFCl/QTbDmnD/Fi3NuaO\nX9it5PoGG2kVaH5SR3A9cFj7Q9aedyyShxBCCJkLNjC49XuP+NdVRf1tYj36YT64RB8T77BB1nV+\nGdPvzUf79XN7pCYahVmGdZ4xExR9uEzXGes3/lHaNSniP2fPCwD0NRjfQuMjNaynBoj1Gblft3Go\nSqe7vKjPVcFR4+fVZtq0YnJ/yWykuEnvceByjL9csZY7N0S/yyT0VgvobdByYYsuo+iYHvvcIS0n\nS4wTn3VcAzBaoMu0/rBjxmevYo+p46A+YLhWn4eqx6J19i03QdFrzHVSoS+E0oO6zli/llvP1/20\ngeIBoHKXvl4L9LQdnesWzdLpD/DLHiGEEEIIIYRkIVzsEUIIIYQQQkgWwsUeIYQQQgghhGQhi8bw\n1Jl4crEyE8dmj/an2zOyIlJG4pi21TWmvCi/VAeJiZ2tDdaPPLFUyeddtlvJj+yvj9S5frn2VTvS\nU6rkkVHdptQGXaczgXF2PKd94a550Y5Inb95YJOSHxtqUPLyOm2ALCkT46VC21E3d2v/yCWlvZE6\nG87TfoGxHF1GU3eaoC2EEELIHLC+a6lC46t0xPgN9UZ9dPIGjY+e8fvrq9fp1jctcVyn27hcFY9H\np1qDtbqMXBMvUIxrYU/95O/m48aPsHd1dA+B8l3GR6/CPPuLTaWi04cqjR+h8emzcfwAYGDZ5D55\neWniHmY7I4V6LEdMjLvy/Tp2XG4yqj+Fx/QxZft0Ge1n6gujq1TPNauf1vrRfrbOX7Ujqj+dp+t2\nx43foL0W+5fq422cxpKHdXrb+dE6V/xa6+Rgp+5Hn54SI2ViYg6V6+PzjX+u9TMEgOPnTL40yluE\nsSH5ZY8QQgghhBBCshAu9gghhBBCCCEkC+FijxBCCCGEEEKyEHEuGnOGEEIIIYQQQsjihl/2CCGE\nEEIIISQL4WKPEEIIIYQQQrIQLvYIIYQQQgghJAvhYo8QQgghhBBCshAu9gghhBBCCCEkC+FijxBC\nCCGEEEKyEC72CCGEEEIIISQL4WKPEEIIIYQQQrIQLvYIIYQQQgghJAvhYo8QQgghhBBCshAu9ggh\nhBBCCCEkC+FijxBCCCGEEEKyEC72CCGEEEIIISQL4WKPEEIIIYQQQrIQLvYIIYQQQgghJAvhYo8Q\nQgghhBBCshAu9gghhBBCCCEkC+FijxBCCCGEEEKyEC72CCGEEEIIISQL4WKPEEIIIYQQQrIQLvYI\nIYQQQgghJAvhYo8QQgghhBBCshAu9gghhBBCCCEkC+FijxBCCCGEEEKyEC72CCGEEEIIISQL4WIv\ng4jINhF5y0mq+2YRuTmD9d0mIh8NyY0i0pCp+smJwY5jhuu+SURuy2B9N4vI7SF5m4hszVT9pzrU\nNepaNmHHOMN1bxWRbRms7yYRuT8k3yYiN2WqfnLi4H04O+/DC2axF9ycmmaY52YRGRGRvtDfmvlq\nY6YRkXeJyFER6RGRr4tI/GS3KVOIyOtF5KCI9IvIT0Sk8mS3yTIbnQ3ynSsi2wN9bRWRd85H+04G\ni2Hc5gsRuVpEdonIgIj8VkTqT2DZs7k/3mXujUkR2XGi2rRQCO6NTkTWney2ZAIRaQj6Gx7bD4bS\n48E56QmeH+82+SfUUxFZLiI/FZEOEWkSkbeavC64tsfr/VoobdrP41nqc1xEbgnumR0i8nMRWT6T\nMhYDIvLPwXm+5mS3JROISLWIPCAi7SLSJSK/E5EXh9JvEpExo1dbg7RV5ve+4Ny9J0i/UkR2BOW2\ni8iP56Izs9TbchH5hogcC/5unm39C5lT7T4MACLyFhHZG+jdL0WkLs0x+SLynNUbEXmFiDwT5H1Q\nRDaG0m4UkceCe3iTiHxKRPLm0tYFs9ibA3c454pDf/szUelcT/w0yr8WwPsAXA2gHsAaAB+azzon\nacu89jVNfWcC+AqANwBYAmAAwJcy2Yb5QkSqAfwSvn9VANYB+N8M1T3fOrtgxu0k6Gw1gB8B+CCA\nSgCPArgjk22wOOeuC98bATwI4AeZqDtT519ELgWwNhN1TdKGjOpaiPLQ+H4k9PvNANbDPzeuBPCP\nIvJSYFp6ejuAA/DX78sAfFxErjT1bg7Vay1V5vN5/E4AlwA4G0AdgE4Anz+B5U9IBvV5LYDrARzJ\nRH0TtCHT+twH4E0AagBUAPhXAD837fid0attAOCcO2TucZsApADcGeTbCeBa51w5vM48D+DLGenV\nC3wGQCGABgAXAniDiPY2mkMAACAASURBVPw/maiY9+F5rW8rgI8DeCX8vfQAgO+mOfQfABw3edcD\n+DaAtwIoB/BzAD8L9aEQwN8BqAZwEfw64O/n0t6MLvbEf9F4QkR6ReQHInKHiHxURIoA3AWgLvR2\nJrJCzkD7nIi8Q0T2i0ibiHxaRHKCtJuCt0+fEZF2+AcqRORNwaq9U0R+Jfot6UvEv0HtFpEvAJAZ\nNOdGAP/lnHvWOdcJ4CMAbjqBfW0UkfeLyM6g7beKSCJI2xq8TXiviBwFcGvw+8tF5MngLdmDInJ2\nqLxzROTxYGzvAJCYQ/NuAPBz59x251wf/MTk1SJSMocyZ8U86Oy7AfzKOfdt59ywc67XOffcHNq3\nkMZxXsdNvInFJ0Tk4eCN108l+HIoL3zteLOIHALwm+D3i4M+donIUxIy0RCR1SJyb9DXu+FvrLPl\n1QCedc79wDk3BH9/2Cwip8+gf/N2fxRvQn0ZgG/OJJ8pYyHp2vjD/fMA/na2fZqk7IWsa1NxI4CP\nOOc6g3vLV/HCs2NCPRWRYgBbAXzMOTfinHsKwA/hJ+IzZh70eTX8vbM1aPsdAM6cTduC9i3EMf4i\ngPcCSM62X+kQb5p3i4jcHbTvXtFzFScifyMiz8MviBDoxN3iv6LuFpHXho6vEpGfBeftYcxhou+c\nG3LO7XbOpeDnSGPwi77ZWIW8EcB251xjUHarc64llD4G/4J1QuZBb18B4FPOuYGgXf+FWV5TQft4\nH8aCuA+/HMAPgjl6En6Ofrn4FzZ/qA/AXwD4hMl7LYD7nHP3O+dG4V9wLAdwBQA4577snLvPOZd0\nzjXDLwxfjDmQscWeiOQD+DGA2+Av4u8CeBUAOOf6AVwHoCX0lqZFRC4Vka4pin5FcDN6VkT++gQ0\n9VUAzgdwLvyKPXxRXgRgP/xbz4+JyCsBfAD+AVoD4L6gX+E3qP8Er1D7EBos8eYHXSKyaoJ2nAng\nqZD8FIAlIlI11w6GuAFe6dYCOC1o6zhL4cepHsBfisg5AL4O4K/gv0h9Bf5NRDwY258A+FaQ5wcA\n/myyioO+XzpBsuq7c24f/MPvtJl2cC7Mk85eDKAjuBkdE2+KNJEOTJeMjeMCGLc3wl+TywCMAvic\nSb8CwBkArhVvrvMLAB+F78/fA7hTRGqCY78D4DH46/Mj8JPkCRGRp0Xk9RMk2773w1/z05qMzuP9\ncZw3wj9cGqd5/EQsFF0DgHfBT+yenmOfJmKh6to4B4OJ3a3B8wYiUhG01z47xvVwMj0dfxkZfikp\nAM4y9W4Xbx76I4n6YY8/j3cCuBsnVp//C8CLRaRORArhdfGuSY6fDhkb46nGVESuBzDsnPufOfZp\nIm4I2lUN4En4CWSYP4Wf42wMFjZ3w/epFsD/AfAlecHU7IsAhuDP25swxeJFRP5bRN43xTFPB2X+\nDMDXnHPHQsnniH8Bv0dEPihpvuKIiMCP5zfM76sCvRqEH7NPTdKG+boPT3VNzRTeh1/gZN6H7bgC\nemw/D79GGJxG3sn04nIAz07W1ilxzmXkL2hsMwAJ/XY/gI8G/98KoGmGZW6E/zSfC+BF8KYPr5tD\nGx2Al4bktwH4dfD/mwAcMsffBeDNITkH3nStHl5Bfx9KEwBNAN4yzbbsM22JBe1rmGXfbgZwc0hu\nBPDWkPzHAPaFxiIJIBFK/zL82+JwmbvhL7TLAbSYsX1wfGxD9U2r7QB+HW5b8FszgK2Z0td51Nk9\nALoAXAD/Bu1zAB6YQxszOo6ZHLfgmrstJG8D8MmQvDHoXy68iYwDsCaU/l4A3zJl/gr+Br8K/sFR\nFEr7DoDbTX3Tajv8RPST5rcHANx0snTNlL93um1ZJLq2MuhTWSA7AOvm0LfFpGvF8C8k8+BfPP4Q\n/ovX+HlxZhxeAqBxOnoa6Nzn4e9N5wLoALDb6Gk+vOnRFwA8AyAvdI7Gn8d/Df8V5XWhvHO9d5YB\n+F7Qv1EATwConMOYZ3SMp2hLCfwXtYbQtXbNHPq2FcC2kHwbgO8ZHRoDsDJ0/VwVSv9z+JdD4TK/\nAuBfgvMzAuD0UNrHAdxv6pvx/SbQu9cBuDH02xr4r7o58GaaOwG8P03ey+BNQosnKLsyGMOLJ6l/\nPp75t8O/+C+B/6q4D35RP9uxbQTvwwvhPnwNgDZ4s/KC4PpIIbjnwb8kuCud3gA4HUB/8Hs+vBVU\nagK9fhP82qF6tufVOZdRM846AM0uaH3A4bkU6Jzb6Zxrcc6NOeceBPCfAF6T7ljRmxXcMEmx4TYd\nDNo9UXvrAfxn8OajC/7BKPCfY+vCxwf9nkl/+wCUhuTx//faA8WbZ4z37QMzqGOyvh533lRmnHoA\n7xnva9DflUGedGN7cAbtsNi+I5AjfZ9nTrjOwr/h+bFz7pHg/H4IwItEpMweOINxXSjjOO1xE5EP\nhPp2ywzqsH2NQZtihNPrAVxv+nop/BvCOgCdzr+tDZc3W+aqs/OhawD+4E+xFH5RMNExi03XPgvg\nw8657qkOzDZdc871Oecedc6NOudaAbwdwB+JN5fuCw6zz45xPZxKT2+An1gfhp8k3g4/0Rive7vz\npkVd8D50q+HfqqvnMbw/XQv083iu+vxFAHH4rxNF8BPotF/2ZjDmC2WMb4afpDZOdaCI3BDq20y+\nbIbnI33w85WJ5jf1AC4yfb0B/j5SA/+iwZ67OeO8Sed3AbxPRDYHv+13zh1wzqWcczsAfBjp53k3\nArgz6Fu6sjvgv/r9NN2XwYD5uA+/A/65/zyAn8J/LUy7yQvvw2lZKNeowjl3D/zLjzvhF+CN8PfR\npuDL+Kfgxz5d3l3w+voF+I9U1fAvMewmLn8KbwJ6nXOubbZtBTLrs3cEwPLgU/s4K0P/d5g7DhP4\nxTm9WYE1XwgTbtMq+AfWRG08DOCvnHPlob+CYOF5JFxW0O+VmD7PAtgckjcDaHXOtdsDnXNvDfXt\n4zOoY6Z9/Zjpa2FwY043tnMxTVR9F7+jWxz+q1gmmQ+dfdrkm7CMGYzrQhnHaY+bc+7job691aZP\ngu3rCPzbtT8UHfr/YfgJVLivRc65T8L3tSK4KYfLmy2270XwJjbTNb2Yz/vjjQB+NNEkCFiUunY1\ngE+LNyc8Gvz2u3QmN1moa5bxduQ47999BNFnx7geTqqnzrmDzrmXO+dqnHMXwU9CHp6i7nTP3CPw\ni8gTqc9b4N/6dzjnhuG/QF4ogQmratT0x3yhjPHVAN4R0ueVAL4vIu9N07dvh/p23QzqCM9HiuG/\ndE10/R4GcK/pa7Fz7q/hN5sYRfTcnUhi8F/00hHROREpgN/Y5htpc7xAHrxZqn3hMc4Jvw8H+nqD\nc26pc+5M+Hl32muK9+G0LJRrNIJz7ovOufXOuSXwi748eGuH9fBfHu8LzsuPACwLzlNDkPeHzrmz\nnHNV8IvGBgCPjJctflOtrwJ4RfCSY264OXwWnMkf/KfKQ/BOnHnw/nBJvPB5/HT4tx9lMyjzlfCO\nvAK/y1EzQp//Z9FGB2+KVgGvYLsA/KV74fPy/eb4VwUDe2YglwG4Pvh/Nfwq/9VBf98Jf4Ocrhnn\nSwEchf9sXQ7vfPrJOfTtZkTNOHcAWAF/078fwMddmk/OwW/nw19IFwXnuwh+t7aS0Ni+E/4m/Wr4\nC3K2ZpxnAuiBN8sogn+7/L3Z9n2B6exV8G+9twTn6jMw5jIzbGNGxzGT44b0Jh1NwTVRCO9f8J0g\nrQH++s0LHb8yuIauhTf7SATnZEWQ/nsA/xb0+9Kg7bM16agB0A3v75CAd7j+/Qz6esJ1LchXELTr\nqpnkWwS6Vgv/lWH8z8H7wxacArp2EYAN8JPGKviNSn4bSv8kgHvhn2Onw09yXjodPYX/Sjc+Pn8B\nP6mqCV3fW4L+FcO/1d8NIBakh5/HL4J/3n3rROkz/GYTd8I/Z2PwvjDNc9DnjI7xFG2pgtbnw/CL\nl7QmidMobyuiZpw9Qbvy4Z87D4TSlfldoAMH4XdWjgV/FwA4I0i/A96ktjA4f02YpRkn/HU73q4C\neFO8XgB1Qfp1AJaE9OYZAP9iyng9/P1JzO+vxgvXSg2A7wN4fJK2zMczf20wvrlBX9oQzBlnObaN\n4H0YOPn34QS8j53ALxq3hcYhz5yXV8MvyJcCyA2OOS9o47hefidU9lUA2gFcPls9ibT3RBU0zZNz\nPrxjcF8waD8C8MFQ+teDDnbBf3K9DEDfJOV9Nzi+D35h9o45ts/Bf3bdH5T776GBuQlmsRf8/gb4\nC68nuKC+Hkp7KfxXjW74z7X3IljsBcrRB2DVJO15N4DWoOxbAcTn0LebEV3svR/+03EX/BuxwiBt\nK9LYpQf9eSQ4/kgwhiWhsX0C/iZ9R/A34WIv6Ptlk7T39fA3oX5404dZ+2YsJJ0N8vw1/IuJTvgt\nd1fOoX2ZHseMjRvS3/g/Af9WtCc4d9VBWgPMjT/4/SL4664D/o30L8avOfg3x/cFfbob/hqd8MYP\n//Xjhknaew38fWgwyNsww/7Oh669Dn7SJjNpy2LQNVOPmqxms64FY3oA/ho7Ar/D6tJQejzQlR74\n58e7p6un8Nt9Hw/Kvh/A+aG0q+AXd/0AjsFv7LA+lG6fx58+kfoMP2H+dlB3V9C+C+cw5pke40nv\nH2mutRPts3dL0K4+ANsBrJ7s+oFfJP0i6Gc7/AvnLUFaDYD/Ds7bw/CbXUy42IM3t/3ABG29An7T\noN7gvN6L0CQXfnLeGujdfngzzpgp41cwfmnB73+LF66Vo/AL1Popzt2Jnqe+Fn6iPxCUe+1sxzWk\nG7wPn/z7cDm8pda4bn0CwXphguvRLsLvD+n8V6B9CX8L/7KsL/R311z0RoKCTwoi8hCAW5xzt560\nRoQQEQf/8Np7sttyopEgkKdz7uZAboRfeN6Tofob4S+ixkzUN18sQJ1tRAbHMZOIyE3wOnNTIG+D\nvzF/bZJsJ7L+bfAvSLZlor409VPXMsSprmuZYAHq8zZkcIwzifjt5m92zm0N5NvgJ5v/NEm2E1n/\nbfCLzdsyUd98sgD1thG8D89X/duQpffhTMfZu0JElopInojcCL+LzS8z2QZCZgJ1lmQK6hrJJqjP\nZDFCvSXZSEYjzsObBXwf3mZ4P4DXOOeOZLgNpyrbTnL9n4U3IVhsUGdPHk/i5OrMbfAmM5mCunby\nONV0LRNQn08ejfA6dbL4CRavPlNvTx68D88TJ9WMkxBCCCGEEELI/JBRM05CCCGEEEIIIZmBiz1C\nCCGEEEIIyUIy7bNHCCFkgXLmP35G2fUPV2sz/7Ld+vj+FdF42sMVKSWX7tXvFIev6lHyYHdCycW7\n83UdK8eUnNcXfUeZaNPtGK7Q7R4xbXI5Ol1SOv/qDdpF51hvcaTO5DNlWq4ZixwTpnyZ7rff/DnU\npv+tUnLsuuORMvofqFFy1RW6nUcfW6rkve97d7qA51nF5rdrnXW5Oj1v0OhCSfSUjBRpuahF5+lb\nafIY75dEh5ZHC7UsaVQj1mfaVazrGNOXAYy6QLRKY8hcq3n90X7mG28o286UqXO0QJcZM2Um2nR6\n1+lRt6Cy3fp6HVyqjyk4qst86gvvynqdveDG/1AnwepfolOfo2QanbXjH+/WP3Sv0RdC7rCpo10f\nP1yux8leNwCQN6R/Gy7V7XK5Ws4Z1cen8nR60oS2zxuMVIm8AV3GWEKXIaP6+OGKycssOK7L61kT\nPbdl+/S56V2pz02B0fvHv7Lw77P8skcIIYQQQgghWQgXe4QQQgghhBCShdCMkxBCCICoyctYr7ZO\nabtc2wLFWo3dF4DcpM5T+2eHlPz84SVKljxtMtPXYOxyEsYEsz/6jjInqeWRcp0nXjug5OIC3Y+u\nndqE8vCjy5X8R1c/Hqlz+33nKbl0vzabGlimz0MXtM1S4oh+/I5cok++64yajsYS2nyoaW+tLjON\n6V62I2ZHcQdj0ltuzL7SbEAe69dy55n6oPwuXcZo4eQmmKnY/9/encVIdp7nHX+ra6/q6n3v6enZ\nF+5DUaRMkSYpklLkxJEi2YIdJYARyDCQXAXIcpPAyV0QOIGTOAGCJL4w7DiRYkUOFceMtVAUpSGH\n4jIccmY4M5yevfet9r1yoVzweb/GMAARgH36/7t7u06d9Tun+1T1c16dn/8XOrPw38/8v2323DBv\nDt793x/9vzJXZt3/+ZlZaktn2n/b/bug247GqNapbZ1fbVJfT2/sMP7cj/w0/l9J94K+ju73vpY7\ndtMf/S+V/l+JN4/r9SfhxrTXGNRltN2/ku70r8exjh/n7l+P0+4Nffp6/CP+pXKnWECyrPXQlZbU\n1Qm9jvp/JfX/Ll0f09d3+tdR/y+yftz7fy3dDfhmDwAAAAAiiJs9AAAAAIggbvYAAAAAIILI7AEA\nzMzs6K9rb4XXLx6UevSnGixaf0zzE2Zm8ayGPXxGr39I83PVqgY9ujl9v4tkBY/WNzMrHXIZvTWd\nqO92Qer8MyVdh8PaFmG0X9fx5T/WfJ6ZWf1RDZOkX9TQS6tfVzy9ouvUcY+1z72ZlTpVDHMh9S/q\nerav6nb5rMlesH3c7ed1zeT033S5oOkwF9TR7h+WXdLPwVsuPpl0WVafyfPZpJ0+Vvd5t+yye8R8\n2ufn3Hbsd1nFpNaFK+GJ0nSPpc+7ZXZTd89Ydd1fjHmX+bMdxl/xsNapbZ8D3H35p49r46TLW7oc\n4+BVDXQW94d/qrc0ZmzpDdcWYdgdS5drTjRd+5m2a6PQF54n/jxIb7lzr6TX7q3Dut7++uSzrsMX\nwwG0fVBPnlRJx7Ufk/GmP4/09dySW8YOMdPyPl1msuTOk+QOb/qE45s9AAAAAIggbvYAAAAAIIK4\n2QMAAACACCKzBwAwM7PX39GAzfgZzUesfVrzDvkrYZ+9+pjLz5X1M8XSvE4fK2pTskRN8xCtUc2v\nZNZ3ykvoz+ou35S4R/N116+P6+vr+qtwcb+uU+dA2HQqfl0zepv36OvdnO6H3E3dl726m+GTm1KW\nz7qAlZkVvqu9+louD1Wb3qE5VsT53nGDH+h+L825zI/rmWVm5uJMltDIZpATTbmsks8itXOul9cO\nfbmSGhsNMlatAX1PsqTbkd5wy3R90pI7ZD7zi/qz2qjO0/dWS/j+lTr8rHRA69zt8NwcWHC9/Nw8\nWoXdl3/6uFIu8zm4oNe40qxej3yvOLOw513CXU/aPkfqap/PbGtkeOfefn6cu/d0E3qiDF3R7Uq6\nTF9lRq+zjYFwLPis4fZhHbN9vi3rmsuyuq+0to/oDwYWwpygz8/63pzNHdbzk45v9gAAAAAggrjZ\nAwAAAIAI4mYPAAAAACKImz0AAAAAiCAe0AIAMDOz3C39lbB9+O6NwaszOzy4pOYe+uAC9MlFfahL\n4bq+nnZNlssz/gEu4YMDcisasl9P63reO70o9TtXjkrtHwJj27qOx/6z75JttvIp7TBcndL1ijd0\nPyQqbhEP6ZMHMm/qA1laI+GDA6pTOs9e3B2fafdkkT0gs6J1eUb3kX/AQ9s1hjYLm0NnXINq/0CN\ngas6HuJ1HT/tgo6fbjL8XL3Vr2O0Pqp1+4guI/VeTurKPl3H/hu6jhOvrAbLLN2jnbj9AzZ8c/nC\nDfdwlUFdRto1A++6BtZmZr24TuMfAtMY2ntN1X2T7tqIHvt4Q19v58MHgvh5+Gbi8aa+J1lx83TH\nvucahdcmw2X2uYe8+AcZdQe1HrmgT43xjdrTW7rdyWq4zPVJnabpHlzkx2CqpNfN+rB7gNMVfb2d\n/eiHrXTcnZLfd7sB3+wBAAAAQARxswcAAAAAEcTNHgAAAABEEJk9AICZmVXnW1LH0ppv6NU1P5FZ\nDH+FzDx5S+rrKyM6wW0NPJQOuhlc1QzF6Hldp8wd143azLbuHZJ66JKu9ztpzej90hdel/qFlx+R\nupvTYFF1OgxpjL2rmaqFQxpY6k1qwCV1Shu7xy9qfipxakvqxg3XfdrC4zM8XZS6dMHt6z2gVdDa\nN0DvukhYSneZmZnVJlyuzMUlR85r9ii5oWGl2IZ2ao9NaP6yOe46npvZysP6WXvT5UYHcjp+nvvK\nO1J/+81PSZ24pOdiLxmem/lbut4bJ3Tn1ad13Lfzuo75mzo/n3/s7RB/6rocYKtw98zVXtB1+biu\nxpKD/bhTTrky4zN5ri75ZvauibpGQK2ddZm/enhcavt0fMQa7rxxq3n563rdnHlJX28WdHzlVl12\n2swGFnQZvql602U+N+7RC8Doey4k+v8QEW24bGpjWGuf6d0N+GYPAAAAACKImz0AAAAAiCBu9gAA\nAAAggsjsAQDMzGxmfl3qxWXNwsVc7ybfQ8/M7Nq5GZ3G5Z9yRzWbVt7WXEf+Vf21dP2LrhdTSfNQ\nZmaZVZcvcWU7ryvx3ffvk/pzj5+T+vzv6Oubx10QzMzaWZdHeVl3xuLjGlZaX9FM319+6g2p/9cP\nNDeY2O8a85lZ4l0NSbUvudyfy6/tBT6z48dkdsXlo7QF3v+dRufh+zZ2su74uwxePK/H9vJf19d7\no67Zn5mZ6weWKOoyygUdP6+tHpD66fsvSn3ujI7Zrfv13DUz67mP9yfe0gxocV3PPd+DzPf2G3pf\n51c6EOa8Mmtapze1jvV2X/7p42q4S5jvV5cs6z5JaGTUzMxyyzpNsqp1Y+Du/Q3TLnfWfEivNzvl\nL3ubOiZ77roac5nuREUH3Pp9OtPZl+/eh88s7HE5fVpP8PWTekL73GBpTtdp8ozu7OpU2Bwy48ao\n7/3a2qHv4Scd3+wBAAAAQARxswcAAAAAEcTNHgAAAABEEJk9AICZma1tayYscUfzDJ19mrFojoV5\nm9RFbeCUdNGzZkWzRL0pzWBsHtNfS0F+pRrmJcoHNDsSd/2fZo6vSF16cUrqsy8+oDN0Pagy6x+d\nK6pOaDakPaTbldjS7frRrSNSf/FzP5P6hZ+dCpbR2qfzvP/kDak/WNMM317gs0i5Jd8jy03vMqRm\nZtlVPb7ZFe1xF+vom7aP6ACpj2h+MzapuaD4tbBP4+g5XWZ9VNc796D27ssmNV/3zuq01BuP6tjI\nXnPN28xs9LzuLN+TsO2ySLkl12czrt8PrP6CLjO1GmZby/Nad5PufK7svvzTx5Vw10Tft61Z0H3i\nj5OZWX5Jj2X/dR1z3ZS+aeuojkHfp89rlnYIt/bpevZVXUZv2q3DNc2u5hb9mNcxWrikY97MLHNR\nG2NWHpiVuuXakaY0Em65ZR3Dm8d0PxRuhnnayoxue2lO91Wfu+bsBnyzBwAAAAARxM0eAAAAAEQQ\nN3sAAAAAEEFk9gAAZmaWfFMze5VDmhOKbWiGr5sMA1BTjy9JvbQ+KHWnpDmNzB2tfc+p+kM1qYdH\nNMNhZraypes99h3NVG1UNKPXe7yk6/SGNqjrv6XbNfpf3guWeedv3S+173OVc5mpxoi+XlzVdV4b\n1/qXH3krWOZ3Tz8s9Y0/OSR1/HOuQdQeMPCB1hVt82h9OoSDjJ+ZWXVCP/f2PRTziy7rFtMMT6Lm\nMp13tB9Zv0Yrf77MSV1mdVrn8Y39r0v9d4ZuSv1vt+akHjqmJ84/Xf1asMy1BzRjlb+l6+B7FDZd\nr7b4Dv3ePqw13wh+lryu14yhizrP7WN3n2cU5Vxfx2a/y4S5MRuMLzOrD+uxi9d1zKY3fO5U3+/7\nTfa5PF5sh2t7IqUzafV0JomEvp44rgG6ak0DtFlt62rdd7R3pJlZbExzyOl1HYSpLb3Otl3eOu5y\ngr7XZHE+zCb6a/nANa3L+3bf92S7b40BAAAAAB+Jmz0AAAAAiCBu9gAAAAAggsjsAQDMzKzPZXbM\ntWIa+EA/H2wMhZ8XJo5o1iOR1JkmlzRbknhQcx2tni70n9z751L/49NfDpaZfV9zQcuP+v5gWhde\n0Yze8BUNydx8VrNNq4/cGyxz9C3NcWx+QbOFyQsaHklv6Hbl79HtfvVtDS/dc28Y9Hr4IQ2oXZ4b\nl7q0pn2t9gKfK/N99HwPs94O7cWaQ/rDVNH13Vt0jdFM93NtVMdLsqjnhc8JmZkV79P+Xn/7Mz+U\n+kT6jr5++zNSf+8DHS/PHLos9dyjt4NlLv5on9RbJ/T13KLWH3U9GHtNt3vjgXBDW4O+R6FOkyjv\nvT575iJ4Pj+XXdN95jN8ZmHOL1HTvFy8rJm9ZkHHbCetK9Guafatby3MsrVT7ryY1vOiVtbrcK+q\ntxijt1xueV7HT+wrjwXLLLx8Rerto7odfS2dZ9L1bay731G+P2F7KmxiWJp3WVZ3Tfmo7OonEd/s\nAQAAAEAEcbMHAAAAABHEzR4AAAAARBCZPQCAmZmVjmhIJ3vT5Tie08ZIQ0kf6jG7/fa01D4bkjiu\nOY/6ovaXe/rhC1K/sPag1L/1qZeDZf7+reeknr13WerV07pOPoNR2qe/CrPLmvuoTYc9p1Yf0+zH\n6A80i9gc0OkTT+u+K1W1F1vP9bm6+v2DwTKTn9I+etWqZmSS+R3CPRFXH3P9Dd2xq05q7cejmdn4\nWT2Wudsayolv6pjNvKF9F5PPaP/D5oAe28lfuR4s8/6s9no8X9YGgf/uzDNSpxb1XOy5qNFfrD8g\ndawRZuHSbhhPvaY/qLj8Un1M5zF02WXJ2rovJ86Ey9w86fKLbhJ/Lu4Ffr8mS3fv4xbXOLCZmY1e\naLpp9FrcHtTr0cBNfb2b1mteKe+u9TPhQuNXNIfcHnOZu3XN+aVcHrM6pXXP3YHURsPvn+p/VbOp\nvudgO++u1RMuV+iuB8mqLqNwK/wd1hx055obs0GWdRfgmz0AAAAAiCBu9gAAAAAggrjZAwAAAIAI\n4mYPAAAAACKIB7QAAMzMLHfDNcF9Wrss37w1KnX+cth4N/aga0Dd0hB/e0MfXjF1aE3qe/q1mfTf\nH9FG4r+7eSBYZvuAPlCj9l+ndJ30+SzWHHIz6LqQ/yF9+MHxg67btJktfWde6o0ntIlx4aw+PGW7\nqA83yOZ0GbkJvpbpngAAGV1JREFU3W/dxcFgmc23h3WaIX26Ray19xpUJ91DIIpHdJ9klt0DGcJe\n9cGDITLrOma7y6tSx4f02FRG9YEOT//GGan/0cSPgmW+WN0v9W+/8KtSJ5vuYRbuo/nsir5eGnTN\n4wf0oTNmZuM/1JnUh9wDNtwDV5JFXUZ+Ucf41mH/kKFgkcF6+gcXpTfDB+ZEXaKi21yZ1X2U0eFm\nyWq4jyqTOuYGqvrUkHhZry+tWb0e1cZ1ng+f0uvsZkOvV2Zm15b1oS/517XBuX/AV9sNiM6Ejsnh\nmW1d5pFwmanLuszGiK533D2wpZPRujXgzpPU3R8YZGaWWXPzcA3sfSP33YBv9gAAAAAggrjZAwAA\nAIAI4mYPAAAAACKIzB4AwMzMqkc151G9Nib1+Gn9ldH5qubtzMwqyxrKuf/oLam36prB6LrQxKDr\nIHzkpd+QOvtmmOvo3qPrPXhVs0WVGc0WTTypucA7b2qoL7GmeZj5+zeCZV57YkTqvpbuG59f6hZ1\nntPT4b77sOyz68HPzt/WLGK3rsscHtNG3XtB18VGs0s+o6cZvs3j4Wfc2RWti/OabyrETkidXixK\nXZrV7NtSXc+BPyzeGyzzXGmf1Plbul6lg7re2X16bGtxXUZmUcdCNxXmitpZn2/S15NVreNNnd5n\n9NoZ1zR7Jlymb0Ada2ldmQ3eEnmdtO63tLu8ZNf12Psm62Zm+Ts6TX1cx2zfkJ4YDZddGz2lg77c\n0vdfvaLXGjOzgWu6HpOv64BpDei1ufBpDR+m4prZ21/YlLo9Em7n+Zyux+FBPfeuXNZrd1+/DrB6\nSq+7qQ09V7eOhctM6SKsz43ZZn73ZaP5Zg8AAAAAIoibPQAAAACIIG72AAAAACCCyOwBAMzMLN2v\nWbdsWsMK6w9pg7reNe37Zmb25CMXpL66rbm/bFLneWVhUup/dumXpZ77C9eTaiLMBQ29oDmMvoYu\nY+ycBoc2yzM6vduMxoy+/5XvnAqW2R7RzEz/df3s9Evf0N5qf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size 1080x360 with 10 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"dJMy8jopGkow","colab_type":"code","outputId":"c48a53c5-6be8-4e2c-9a67-45d5982b4f76","executionInfo":{"status":"error","timestamp":1568221932906,"user_tz":240,"elapsed":1664,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":181}},"source":["avgs_codes.shape\n","p = torch.mean(avgs_codes, dim=0)\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-45-22d83b23d3f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mavgs_codes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavgs_codes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'avgs_codes' is not defined"]}]},{"cell_type":"code","metadata":{"id":"SQRT2T8nGkkb","colab_type":"code","outputId":"1e31c8be-f4c8-4eea-ae72-b07e93c1138a","executionInfo":{"status":"error","timestamp":1568314035172,"user_tz":240,"elapsed":1522,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":334}},"source":["for i in range(10):\n","  z = model.decode(p[i].cuda()).cuda()  \n","  plt.figure()\n","  plt.imshow(z.detach().cpu().view(28,28))"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-49-40c06c632afa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 539\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'autoencoder' object has no attribute 'decode'"]}]},{"cell_type":"code","metadata":{"id":"G_DVHFElGkhQ","colab_type":"code","outputId":"085c8621-22f5-4ae9-c835-8c899eeeb262","executionInfo":{"status":"ok","timestamp":1568314126536,"user_tz":240,"elapsed":1443,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# z.shape\n","plt.imshow(avgs[0,6].reshape(28,28))\n","# avgs[0].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f4680560588>"]},"metadata":{"tags":[]},"execution_count":59},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"tURLOuOs7ehU","colab_type":"code","outputId":"8790d184-95ea-45d9-c3a6-6462d9dc993f","executionInfo":{"status":"error","timestamp":1567697028825,"user_tz":240,"elapsed":526504,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":766}},"source":["num_epochs = 20\n","\n","def resize_tensor(input_tensors, h, w):\n","  final_output = None\n","  batch_size, channel, height, width = input_tensors.shape\n","  input_tensors = torch.squeeze(input_tensors, 1)\n","  \n","  for img in input_tensors:\n","    img_PIL = transforms.ToPILImage()(img)\n","    img_PIL = torchvision.transforms.Resize([h,w])(img_PIL)\n","    img_PIL = torchvision.transforms.ToTensor()(img_PIL)\n","    if final_output is None:\n","      final_output = img_PIL\n","    else:\n","      final_output = torch.cat((final_output, img_PIL), 0)\n","  final_output = torch.unsqueeze(final_output, 1)\n","  return final_output\n","\n","def custom_loss(output1, output2, img): #nn.MSELoss()\n","#     import pdb; pdb.set_trace()\n","    h, w = 10, 10 #output2.shape[2:]\n","    img_lr = img.view(-1, 1, 28, 28)\n","    img_lr = resize_tensor(img_lr.cpu(), h, w )\n","#     \n","\n","    img_lr = img_lr.reshape(-1,h*w)\n","#     out2 = output2.view(-1, 1, 28, 28)\n","    return  nn.MSELoss()(output1, img_lr.cuda()) + nn.MSELoss()(output2, img)\n","\n","\n","class autoencoder_two_branch(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.encoder = nn.Sequential(\n","            nn.Linear(28 * 28, 128),\n","            nn.ReLU(True),\n","            nn.Linear(128, 64),\n","            nn.ReLU(True), \n","            nn.Linear(64, 12), \n","            nn.ReLU(True), \n","            nn.Linear(12, 9))\n","        \n","        self.decoder1 = nn.Sequential(\n","            nn.Linear(9, 12),\n","            nn.ReLU(True),\n","            nn.Linear(12, 64),\n","            nn.ReLU(True),\n","            nn.Linear(64, 128),\n","            nn.ReLU(True), \n","            nn.Linear(128, 10 * 10), \n","            nn.Tanh())\n","\n","        self.decoder2 = nn.Sequential(\n","            nn.Linear(9, 12),\n","            nn.ReLU(True),\n","            nn.Linear(12, 64),\n","            nn.ReLU(True),\n","            nn.Linear(64, 128),\n","            nn.ReLU(True), \n","            nn.Linear(128, 28 * 28), \n","            nn.Tanh())\n","        \n","        \n","    def forward(self, x):\n","        z = self.encoder(x)\n","        x1 = self.decoder1(z) # low res\n","        x2 = self.decoder2(z)         # high res\n","        return x1, x2, z\n","\n","\n","model = autoencoder_two_branch().cuda()\n","# criterion = nn.MSELoss()\n","# our_criterion =  #nn.MSELoss()\n","\n","optimizer = torch.optim.Adam(\n","    model.parameters(), lr=learning_rate, weight_decay=1e-5)\n","\n","for epoch in range(num_epochs):\n","    for data in dataloader:\n","#         import pdb; pdb.set_trace()      \n","        img,_ = data\n","\n","        img = img.view(img.size(0), -1)\n","        img = Variable(img).cuda()\n","        # ===================forward=====================\n","        output1, output2, _ = model(img)   # low res, high res\n","        loss = custom_loss(output1, output2, img)\n","        # ===================backward====================\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","    # ===================log========================\n","    print('epoch [{}/{}], loss:{:.4f}'\n","          .format(epoch + 1, num_epochs, loss.data))\n","    if epoch % 10 == 0:\n","        pic_lr = to_img(output1.cpu().data, 10, 10)\n","        save_image(pic_lr, './mlp_img/image_{}.png'.format(epoch))\n","        pic_hr = to_img(output2.cpu().data, 28, 28)\n","        save_image(pic_hr, './mlp_img/image_{}.png'.format(epoch))\n","\n","torch.save(model.state_dict(), './sim_autoencoder_two_branch.pth')\n","torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/sim_autoencoder_two_branch.pth')\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Exception ignored in: <bound method Image.__del__ of <PIL.Image.Image image mode=L size=28x28 at 0x7F74776297F0>>\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.6/dist-packages/PIL/Image.py\", line 585, in __del__\n","    if (hasattr(self, 'fp') and hasattr(self, '_exclusive_fp')\n","KeyboardInterrupt\n"],"name":"stderr"},{"output_type":"stream","text":["epoch [1/20], loss:0.0554\n","epoch [2/20], loss:0.0498\n","epoch [3/20], loss:0.0451\n","epoch [4/20], loss:0.0452\n","epoch [5/20], loss:0.0422\n","epoch [6/20], loss:0.0366\n","epoch [7/20], loss:0.0382\n","epoch [8/20], loss:0.0392\n","epoch [9/20], loss:0.0404\n","epoch [10/20], loss:0.0366\n","epoch [11/20], loss:0.0365\n","epoch [12/20], loss:0.0358\n","epoch [13/20], loss:0.0341\n","epoch [14/20], loss:0.0367\n","epoch [15/20], loss:0.0364\n","epoch [16/20], loss:0.0334\n","epoch [17/20], loss:0.0357\n","epoch [18/20], loss:0.0343\n","epoch [19/20], loss:0.0399\n","epoch [20/20], loss:0.0363\n"],"name":"stdout"},{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-18-2d64fd352844>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./sim_autoencoder_two_branch.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./drive/My Drive/AutoEncoderProject/sim_autoencoder_two_branch.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \"\"\"\n\u001b[0;32m--> 224\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_with_file_like\u001b[0;34m(f, mode, body)\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mnew_fd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './drive/My Drive/AutoEncoderProject/sim_autoencoder_two_branch.pth'"]}]},{"cell_type":"code","metadata":{"id":"3OKtBnfR-P8q","colab_type":"code","colab":{}},"source":["# img.shape\n","# output2.shape\n","    height, width = 10, 10#output2.shape[2:]\n","    img_lr = img.view(-1, 1, 28, 28)\n","    img_lr = resize_tensor(img_lr.cpu(), height, width)\n","#     \n","\n","    img_lr = img_lr.resize(-1,height*width)\n","#     out2 = output2.view(-1, 1, 28, 28)\n","    return  nn.MSELoss()(output1, img_lr) + nn.MSELoss()(output2, img)\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XV8Mf0c2NbxV","colab_type":"code","outputId":"50a603eb-5ce7-4b3c-b373-df97613926eb","executionInfo":{"status":"ok","timestamp":1563137732129,"user_tz":240,"elapsed":441,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# output2.shape\n","img2 = img.view(-1, 1, 28, 28)\n","img2.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"CKctBQr2NgE9","colab_type":"code","colab":{}},"source":["# img[:,::2].shape\n","img2 = img.reshape(-1, 1, 28, 28)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lJPdhKVDOJMm","colab_type":"code","outputId":"5809a6b3-588c-4abf-fe88-04064dda4305","executionInfo":{"status":"ok","timestamp":1563136963753,"user_tz":240,"elapsed":385,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import math\n","math.sqrt(392)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["19.79898987322333"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"X9kOjzm4OR1V","colab_type":"code","outputId":"30cee694-f209-41a5-b138-20cf1f38d786","executionInfo":{"status":"ok","timestamp":1563137162434,"user_tz":240,"elapsed":443,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["img2.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"2HOF7VGPPDrF","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"R887Qsu8QCYN","colab_type":"code","colab":{}},"source":["a = resize_tensor(img2.cpu(), 10, 10)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"t9KnopZ0QIHo","colab_type":"code","outputId":"cfa813a9-087b-4ff4-f699-dd305b5fd0ce","executionInfo":{"status":"ok","timestamp":1563137473668,"user_tz":240,"elapsed":923,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["a.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 1, 10, 10])"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"id":"s-4czEtpQPih","colab_type":"code","outputId":"73d8aa45-6af1-4596-b82d-2c1076052c1b","executionInfo":{"status":"ok","timestamp":1563137538170,"user_tz":240,"elapsed":1050,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import matplotlib.pyplot as plt\n","plt.imshow(a[0,0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc330b6b438>"]},"metadata":{"tags":[]},"execution_count":52},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOTKYijsLAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"nakb9ezsQSWU","colab_type":"code","outputId":"aa81661f-417f-4530-a344-26e71a6d5b2e","executionInfo":{"status":"ok","timestamp":1563137566515,"user_tz":240,"elapsed":403,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(img2.cpu()[0,0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc3302a7588>"]},"metadata":{"tags":[]},"execution_count":55},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"8F2iykRBQigc","colab_type":"code","outputId":"c2bfdf13-c81f-44f6-83f8-2b4418b1e295","executionInfo":{"status":"ok","timestamp":1563140671391,"user_tz":240,"elapsed":3845,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import matplotlib.pyplot as plt\n","pallete = np.zeros(10*28,28*4)\n","\n","for i in range(10):\n","  \n","  f, axarr = plt.subplots(1, 4)\n","\n","  c = img[i].cpu().detach()\n","  c = c.reshape([28,28])  \n","  axarr[0].imshow(c.numpy())\n","  axarr[0].set_title('image')  \n","  \n","  pallete[:i*28, :28] = c\n","  \n","  output_lr, output_hr, code = model(img[i])\n","\n","  c = code.cpu().detach()\n","  c = c.reshape([3,3])\n","  axarr[1].imshow(c.numpy())\n","  axarr[1].set_title('code')   \n","  \n","  pallete[:i*28, :28] = c\n","  \n","  a = output_lr.reshape([10,10])\n","  a = a.cpu().detach()\n","  axarr[2].imshow(a.numpy())\n","  axarr[2].set_title('low-res recon')  \n","\n","  pallete[:i*28, :28] = a\n","  \n","  \n","  a = output_hr.reshape([28,28])\n","  a = a.cpu().detach()\n","  axarr[3].imshow(a.numpy())\n","  axarr[3].set_title('high-res recon')  \n","\n","  pallete[:i*28, :28] = c \n"," \n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAB4CAYAAAAJ4bKfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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GvOSdhgdOzdK2dZA3dC4QqmIyKRjqCDbQHycVDb3TuxwMfBV4SkUxLv4K9ePc6P+Nr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\nd0VRlDJEhbuiKEoZosJdURSlDPn/JP1XvyeASygAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"mw9L6hbLZaOO","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}